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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation | Type | Conference Article | ||
| Year | 2021 | Publication | 28th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 19-22 | ||
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| Abstract | This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach. | ||||
| Address | Anchorage-Alaska; USA; September 2021 | ||||
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| Area | Expedition | Conference  |
ICIP | ||
| Notes | MSIAU; 600.130; 600.122; 601.349 | Approved | no | ||
| Call Number | Admin @ si @ SSV2021b | Serial | 3579 | ||
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| Author | Aitor Alvarez-Gila; Joost Van de Weijer; Yaxing Wang; Estibaliz Garrote | ||||
| Title | MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation | Type | Conference Article | ||
| Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | multi-view; cross-view; semantic segmentation; synthetic dataset | ||||
| Abstract | We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups 1 . | ||||
| Address | Bordeaux; France; October2022 | ||||
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ICIP | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ AWW2022 | Serial | 3781 | ||
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| Author | Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva | ||||
| Title | Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods | Type | Conference Article | ||
| Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring | ||||
| Abstract | Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans. | ||||
| Address | Bordeaux; France; October 2022 | ||||
| Corporate Author | Thesis | ||||
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ICIP | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ SBC2022 | Serial | 3789 | ||
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| Author | Chengyi Zou; Shuai Wan; Marta Mrak; Marc Gorriz Blanch; Luis Herranz; Tiannan Ji | ||||
| Title | Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding | Type | Conference Article | ||
| Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Video coding; Quantization (signal); Computational modeling; Neural networks; Predictive models; Video compression; Syntactics | ||||
| Abstract | In video compression the luma channel can be useful for predicting chroma channels (Cb, Cr), as has been demonstrated with the Cross-Component Linear Model (CCLM) used in Versatile Video Coding (VVC) standard. More recently, it has been shown that neural networks can even better capture the relationship among different channels. In this paper, a new attention-based neural network is proposed for cross-component intra prediction. With the goal to simplify neural network design, the new framework consists of four branches: boundary branch and luma branch for extracting features from reference samples, attention branch for fusing the first two branches, and prediction branch for computing the predicted chroma samples. The proposed scheme is integrated into VVC test model together with one additional binary block-level syntax flag which indicates whether a given block makes use of the proposed method. Experimental results demonstrate 0.31%/2.36%/2.00% BD-rate reductions on Y/Cb/Cr components, respectively, on top of the VVC Test Model (VTM) 7.0 which uses CCLM. | ||||
| Address | Bordeaux; France; October 2022 | ||||
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ICIP | ||
| Notes | MACO | Approved | no | ||
| Call Number | Admin @ si @ ZWM2022 | Serial | 3790 | ||
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| Author | Adriana Romero; Nicolas Ballas; Samira Ebrahimi Kahou; Antoine Chassang; Carlo Gatta; Yoshua Bengio | ||||
| Title | FitNets: Hints for Thin Deep Nets | Type | Conference Article | ||
| Year | 2015 | Publication | 3rd International Conference on Learning Representations ICLR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Computer Science ; Learning; Computer Science ;Neural and Evolutionary Computing | ||||
| Abstract | While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network. | ||||
| Address | San Diego; CA; May 2015 | ||||
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| Area | Expedition | Conference |
ICLR | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ RBK2015 | Serial | 2593 | ||
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| Author | Marc Masana; Joost Van de Weijer; Andrew Bagdanov | ||||
| Title | On-the-fly Network pruning for object detection | Type | Conference Article | ||
| Year | 2016 | Publication | International conference on learning representations | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Object detection with deep neural networks is often performed by passing a few
thousand candidate bounding boxes through a deep neural network for each image. These bounding boxes are highly correlated since they originate from the same image. In this paper we investigate how to exploit feature occurrence at the image scale to prune the neural network which is subsequently applied to all bounding boxes. We show that removing units which have near-zero activation in the image allows us to significantly reduce the number of parameters in the network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that up to 40% of units in some fully-connected layers can be entirely eliminated with little change in the detection result. |
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| Address | Puerto Rico; May 2016 | ||||
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ICLR | ||
| Notes | LAMP; 600.068; 600.106; 600.079 | Approved | no | ||
| Call Number | Admin @ si @MWB2016 | Serial | 2758 | ||
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| Author | Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville | ||||
| Title | PixelVAE: A Latent Variable Model for Natural Images | Type | Conference Article | ||
| Year | 2017 | Publication | 5th International Conference on Learning Representations | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Deep Learning; Unsupervised Learning | ||||
| Abstract | Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and generate samples that preserve global structure but tend to suffer from image blurriness. PixelCNNs model sharp contours and details very well, but lack an explicit latent representation and have difficulty modeling large-scale structure in a computationally efficient way. In this paper, we present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. The resulting architecture achieves state-of-the-art log-likelihood on binarized MNIST. We extend PixelVAE to a hierarchy of multiple latent variables at different scales; this hierarchical model achieves competitive likelihood on 64x64 ImageNet and generates high-quality samples on LSUN bedrooms. | ||||
| Address | Toulon; France; April 2017 | ||||
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ICLR | ||
| Notes | ADAS; 600.085; 600.076; 601.281; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ GKA2017 | Serial | 2815 | ||
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| Author | Pau Rodriguez; Jordi Gonzalez; Jordi Cucurull; Josep M. Gonfaus; Xavier Roca | ||||
| Title | Regularizing CNNs with Locally Constrained Decorrelations | Type | Conference Article | ||
| Year | 2017 | Publication | 5th International Conference on Learning Representations | Abbreviated Journal | |
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| Address | Toulon; France; April 2017 | ||||
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ICLR | ||
| Notes | ISE; 602.143; 600.119; 600.098 | Approved | no | ||
| Call Number | Admin @ si @ RGC2017 | Serial | 2927 | ||
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| Author | Yaxing Wang; Joost Van de Weijer; Lu Yu; Shangling Jui | ||||
| Title | Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data | Type | Conference Article | ||
| Year | 2022 | Publication | 10th International Conference on Learning Representations | Abbreviated Journal | |
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| Abstract | Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data.
Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e.g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems. To initialize the conditional and reference branch (from a unconditional GAN) we exploit the style mixing characteristics of high-quality GANs to generate an infinite supply of style-mixed triplets to perform the knowledge distillation. Extensive experimental results in a number of image generation tasks (i.e., image-to-image, semantic segmentation-to-image, text-to-image and audio-to-image) demonstrate qualitatively and quantitatively that our method successfully transfers knowledge to the synthetic image generation modules, resulting in more realistic images than previous methods as confirmed by a significant drop in the FID. |
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| Address | Virtual | ||||
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ICLR | ||
| Notes | LAMP; 600.147 | Approved | no | ||
| Call Number | Admin @ si @ WWY2022 | Serial | 3791 | ||
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| Author | Simone Zini; Alex Gomez-Villa; Marco Buzzelli; Bartlomiej Twardowski; Andrew D. Bagdanov; Joost Van de Weijer | ||||
| Title | Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training | Type | Conference Article | ||
| Year | 2023 | Publication | 11th International Conference on Learning Representations | Abbreviated Journal | |
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| Abstract | Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze how the color jitter traditionally used in data augmentation negatively impacts the quality of the color features in learned feature representations. To address this problem, we propose a more realistic, physics-based color data augmentation – which we call Planckian Jitter – that creates realistic variations in chromaticity and produces a model robust to illumination changes that can be commonly observed in real life, while maintaining the ability to discriminate image content based on color information. Experiments confirm that such a representation is complementary to the representations learned with the currently-used color jitter augmentation and that a simple concatenation leads to significant performance gains on a wide range of downstream datasets. In addition, we present a color sensitivity analysis that documents the impact of different training methods on model neurons and shows that the performance of the learned features is robust with respect to illuminant variations. | ||||
| Address | 1 -5 May 2023, Kigali, Ruanda | ||||
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ICLR | ||
| Notes | LAMP; 600.147; 611.008; 5300006 | Approved | no | ||
| Call Number | Admin @ si @ ZGB2023 | Serial | 3820 | ||
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| Author | Fadi Dornaika; Bogdan Raducanu | ||||
| Title | Constructing Panoramic Views Through Facial Gaze Tracking | Type | Conference Article | ||
| Year | 2008 | Publication | IEEE International Conference on Multimedia and Expo, | Abbreviated Journal | |
| Volume | Issue | Pages | 969–972 | ||
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| Address | Hannover (Germany) | ||||
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ICME | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ DoR2008b | Serial | 983 | ||
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| Author | Xavier Baro; Sergio Escalera; Petia Radeva; Jordi Vitria | ||||
| Title | Visual Content Layer for Scalable Recognition in Urban Image Databases, Internet Multimedia Search and Mining | Type | Conference Article | ||
| Year | 2009 | Publication | 10th IEEE International Conference on Multimedia and Expo | Abbreviated Journal | |
| Volume | Issue | Pages | 1616–1619 | ||
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| Abstract | Rich online map interaction represents a useful tool to get multimedia information related to physical places. With this type of systems, users can automatically compute the optimal route for a trip or to look for entertainment places or hotels near their actual position. Standard maps are defined as a fusion of layers, where each one contains specific data such height, streets, or a particular business location. In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (> 500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. This allows an efficient and scalable way of accessing maps by visual content. | ||||
| Address | New York (USA) | ||||
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| ISSN | ISBN | 978-1-4244-4291-1 | Medium | ||
| Area | Expedition | Conference |
ICME | ||
| Notes | OR;MILAB;HuPBA;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ BER2009 | Serial | 1189 | ||
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| Author | D. Jayagopi; Bogdan Raducanu; D. Gatica-Perez | ||||
| Title | Characterizing conversational group dynamics using nonverbal behaviour | Type | Conference Article | ||
| Year | 2009 | Publication | 10th IEEE International Conference on Multimedia and Expo | Abbreviated Journal | |
| Volume | Issue | Pages | 370–373 | ||
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| Abstract | This paper addresses the novel problem of characterizing conversational group dynamics. It is well documented in social psychology that depending on the objectives a group, the dynamics are different. For example, a competitive meeting has a different objective from that of a collaborative meeting. We propose a method to characterize group dynamics based on the joint description of a group members' aggregated acoustical nonverbal behaviour to classify two meeting datasets (one being cooperative-type and the other being competitive-type). We use 4.5 hours of real behavioural multi-party data and show that our methodology can achieve a classification rate of upto 100%. | ||||
| Address | New York, USA | ||||
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| ISSN | 1945-7871 | ISBN | 978-1-4244-4290-4 | Medium | |
| Area | Expedition | Conference |
ICME | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ JRG2009 | Serial | 1217 | ||
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| Author | H. Emrah Tasli; Cevahir Çigla; Theo Gevers; A. Aydin Alatan | ||||
| Title | Super pixel extraction via convexity induced boundary adaptation | Type | Conference Article | ||
| Year | 2013 | Publication | 14th IEEE International Conference on Multimedia and Expo | Abbreviated Journal | |
| Volume | Issue | Pages | 1-6 | ||
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| Abstract | This study presents an efficient super-pixel extraction algorithm with major contributions to the state-of-the-art in terms of accuracy and computational complexity. Segmentation accuracy is improved through convexity constrained geodesic distance utilization; while computational efficiency is achieved by replacing complete region processing with boundary adaptation idea. Starting from the uniformly distributed rectangular equal-sized super-pixels, region boundaries are adapted to intensity edges iteratively by assigning boundary pixels to the most similar neighboring super-pixels. At each iteration, super-pixel regions are updated and hence progressively converging to compact pixel groups. Experimental results with state-of-the-art comparisons, validate the performance of the proposed technique in terms of both accuracy and speed. | ||||
| Address | San Jose; USA; July 2013 | ||||
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| ISSN | 1945-7871 | ISBN | Medium | ||
| Area | Expedition | Conference |
ICME | ||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ TÇG2013 | Serial | 2367 | ||
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| Author | Jaime Moreno; Xavier Otazu | ||||
| Title | Image compression algorithm based on Hilbert scanning of embedded quadTrees: an introduction of the Hi-SET coder | Type | Conference Article | ||
| Year | 2011 | Publication | IEEE International Conference on Multimedia and Expo | Abbreviated Journal | |
| Volume | Issue | Pages | 1-6 | ||
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| Abstract | In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. The implementation of the proposed coder is developed for gray-scale and color image compression. Hi-SET compressed images are, on average, 6.20dB better than the ones obtained by other compression techniques based on the Hilbert scanning. Moreover, Hi-SET improves the image quality in 1.39dB and 1.00dB in gray-scale and color compression, respectively, when compared with JPEG2000 coder. | ||||
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| ISSN | 1945-7871 | ISBN | 978-1-61284-348-3 | Medium | |
| Area | Expedition | Conference |
ICME | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ MoO2011a | Serial | 2176 | ||
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